Few-Image NeRFs.
To address the original NeRF's requirement of many posed images, several methods were proposed. The seminal work PixelNerf [1]} is the first to reduce the image data requirements in order to learn a NeRF by using a trained CNN prior that can allow for transferable representation between scenes. Similarly, MVSNerf [2]}, [3]}, AutoRF [4]}, and ShaRF [5]} learn a CNN prior to generalize across scenes. IBRNet [6]} learns to render novel views based on neighboring views and optimized neural volume representation. More recently, VisionNerf [7]} proposes to use a ViT [8]} to extract global features from the input images to enhance the capability of the NeRF MLP to predict the radiance field when one image is used as input. Unlike these works, we propose SuRFNet to directly learn from the 3D sparse radiance fields. Such a 3D setup benefits from structured 3D learning, creating a 3D prior that guarantees multi-view consistency while benefiting from the speed of recent voxel-based methods. A concurrent work by Guo [3]} learns a 3D prior based on a perceptual loss, but does not use 3D supervision and only uses dense voxels ( limiting the pipeline to the low resolution of \(64^3\)  ).
